The current paradigm of evaluating Large Language Models (LLMs) through static benchmarks comes with significant limitations, such as vulnerability to data contamination and a lack of adaptability to the evolving capabilities of LLMs.

Input reduction reduces the length of the input context before feeding to downstream LLMs.

How can diffusion models process 3D geometries in a coarse-to-fine manner, akin to our multiscale view of the world?

Predicting low-energy molecular conformations given a molecular graph is an important but challenging task in computational drug discovery. Existing state-of-the-art approaches either resort to large scale transformer-based models that diffuse over conformer fields, or use computationally expensive methods to generate initial structures and diffuse over torsion angles.

We address these challenges by investigating the primary contributors to error accumulation in DDIM inversion and identify the singularity problem in traditional noise schedules as a key issue.

Currently, two mainstream GNN-based methods have been developed for constructing force fields: group theory-based methods and direction-based methods.